The present application relates to computer technology, and more specifically, to improving the efficiency of training an artificial neural network system, which may be referred to as a neural network system.
A typical neural network uses layers of non-linear “hidden” units between inputs and outputs of the neural network. Each unit has a weight that is determined during learning, which is referred to as a training stage. In the training stage, a training set of data (for example, a training set of inputs each having a known output) is processed by the neural network. Thus, it is intended that the neural network learn how to provide an output for new input data by generalizing the information the neural network learns in the training stage from the training data. Generally, once learning is complete, a validation set is processed by the neural network to validate the results of learning. Finally, test data (for example, data for which generating an output is desired) can be processed by a validated neural network.